Normalization as a canonical neural computation
Open accessAbstract
There is increasing evidence that the brain relies on a set of canonical neural computations, repeating them across brain regions and modalities to apply similar operations to different problems. A promising candidate for such a computation is normalization, in which the responses of neurons are divided by a common factor that typically includes the summed activity of a pool of neurons. Normalization was developed to explain responses in the primary visual cortex and is now thought to operate throughout the visual system, and in many other sensory modalities and brain regions. Normalization may underlie operations such as the representation of odours, the modulatory effects of visual attention, the encoding of value and the integration of multisensory information. Its presence in such a diversity of neural systems in multiple species, from invertebrates to mammals, suggests that it serves as a canonical neural computation.
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Gain modulation allows input sensitivity to be actively regulated while maintaining the neuron's selectivity for input features.
“Gain modulation allows this input sensitivity to be actively regulated while maintaining the neuron’s selectivity for input features5.”
From Mechanisms underlying gain modulation in the cortex (2020) · cites this paper as [5]
Cortical GABAergic inhibition has a crucial role in regulating the gain of sensory responses.
“Although recent work has identified some potential mechanisms for excitatory regulation of cortical gain97,98, several lines of evidence suggest that cortical GABAergic inhibition has a crucial role in regulating the gain of sensory responses5,99,100 (FIG. 1).”
From Mechanisms underlying gain modulation in the cortex (2020) · cites this paper as [5]
Adaptation enables high sensitivity to small changes in stimulus features over a large range of intensities and is found across sensory systems.
“Adaptation enables high sensitivity to small changes in stimulus features over a large range of intensities, and is found across sensory systems5,17,26,105,210–216.”
From Mechanisms underlying gain modulation in the cortex (2020) · cites this paper as [5]
Computational neuroscience has developed mathematical models of elementary computational components including sensory coding, normalization, working memory, evidence accumulation and decision mechanisms, and motor control.
“These include components for sensory coding29,30, normalization31, working memory32, evidence accumulation and decision mechanisms33–35, and motor control36.”
From Cognitive computational neuroscience (2018) · cites this paper as [31]
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- Authors
- Carandini, M., & Heeger, D. J.
- Affiliations
- University College London (GB)
- New York University (US)
- Corresponding author
- Carandini, Matteo
- Venue
- Nature Reviews Neuroscience
- Published
- 2011-11-23
- Volume
- 13
- Issue
- 1
- Pages
- 51-62
- ISSN
- 1471-003X (ISSN-L)
- 1471-0048
- PMC
- PMC3273486
- OpenAlex
- W1988013646
- Cited by
- 1,906
- References
- 143
- Citations by year
- 2026: 92 · 2025: 135 · 2024: 141 · 2023: 153 · 2022: 124 · 2021: 134 · 2020: 195 · 2019: 161 · 2018: 141 · 2017: 132 · 2016: 146 · 2015: 122 · 2014: 96 · 2013: 92 · 2012: 41
- FWCI
- 6.21
- Type
- journal article
- Language
- en
- Update notices
- erratum (2013-01-18)
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature reviews. Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- National Eye Institute (R01EY019693, R01EY016752)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Keywords
- normalization, primary visual cortex, visual attention, multisensory integration, olfactory representation, value encoding, cross-species generalization
- MeSH terms
- Adaptation, Physiological, Afferent Pathways, Animals, Attention, Humans, Models, Neurological, Neurons, Photic Stimulation, Visual Cortex
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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